The increasing concerns about data privacy and security drive an emerging
field of studying privacy-preserving machine learning from isolated data
sources, i.e., federated learning. A class of federated learning, vertical
federated learning, where different parties hold different features for common
users, has a great potential of driving a more variety of business cooperation
among enterprises in many fields. In machine learning, decision tree ensembles
such as gradient boosting decision tree (GBDT) and random forest are widely
applied powerful models with high interpretability and modeling efficiency.
However, the interpretability is compromised in state-of-the-art vertical
federated learning frameworks such as SecureBoost with anonymous features to
avoid possible data breaches. To address this issue in the inference process,
in this paper, we propose Fed-EINI to protect data privacy and allow the
disclosure of feature meaning by concealing decision paths with a
communication-efficient secure computation method for inference outputs. The
advantages of Fed-EINI will be demonstrated through both theoretical analysis
and extensive numerical results.

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